What is Data Visualization?

One of the most useful tools available to Data Analysts that helps them to present complicated material in an accessible way is data visualization. This rapidly evolving field is focused on using visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data aren’t just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve the desired outcome.

There are many kinds of data visualizations, each of which serves a specific professional purpose. Some of the most popular techniques for conveying information are:

  • Histograms
  • Waterfall charts
  • Area charts
  • Scatter plots
  • Infographics
  • Maps
  • Pie charts
  • Bar charts
  • Box-and-whisker plots
  • Heat maps

Because we live in an increasingly visual culture, those who know how to present information in visually engaging stories have the power not only to help make sense of past events but to offer predictions for the future as well.

There are many data visualization tools and libraries currently available to help transform raw data into visual stories that convey actionable insights. This article will explore Matplotlib, a Python data visualization library, and the benefits and (few) drawbacks to using it at your workplace.

What is Matplotlib?

A good starting point for those who want to learn about Matplotlib is by taking a closer look at Python. Python is a high-level, object-oriented programming language whose straightforward syntax lends itself to readability. Because its basis is English syntax, Python is one of the easiest coding languages to learn. This multipurpose programming language is applicable to almost any situation that uses data, lines of code, or mathematical computations. It allows users to perform advanced data manipulations as well as numerical analysis by using data frames.

Matplotlib is a two-dimensional Python data visualization and plotting library. It was written by John Hunter in 2002. In Hunter’s words, “Matplotlib tries to make easy things easy and hard things possible.” This multi-platform library was created on NumPy arrays and was intended to work with the SciPy stack. It is used in Python and IPython shells, as well as web application servers and Jupyter Notebook.

Matplotlib allows users to write a single script that can be used for flexible data parsing and plotting. This free, open-source library supports many output types, which allows it to be used on any operating system. In addition, it’s helpful for modeling machine learning technologies.

Matplotlib is particularly suited to working with numerical information that needs to be visually conveyed. It is able to create publishable, high-quality graphs with much less effort than other data visualization tools. Matplotlib is used by Data Analysts around the world to design engaging and stunning figures, charts, and graphs. This extensive library can change even the most minute details of a figure to enhance the subsequent visualization.

Many companies and businesses use Matplotlib for their data visualization needs, such as Nordstrom, WellsFargo, and Cigna.

How is Matplotlib Used by Data Analysts?

Matplotlib has a wide range of uses for those working in data science or data analytics. The following are a few of its most popular features for Analysts looking to create visualizations based on data:

  • Plots can be easily made using Matplotlib, since most plots are created by following the exact same steps.
  • Matplotlib comes with several plot options. These allow users to identify patterns and trends, and to make correlations.
  • This library includes an object-oriented API, which is useful for embedding plots into various applications.
  • Matplotlib is useful for those who wish to create bar graphs to compare and contrast data in different categories or track changes during a given period of time.
  • When working with Matplotlib’s scatter plots, it’s easy to spot outliers.
  • In situations where numerical proportion must be communicated, Data Analysts can create pie charts using Matplotlib. These charts depict the proportions of a part to the whole.
  • Matplotlib is a powerful tool for designing histograms, which are essential for counting the variables in a plot.
  • Those who wish to monitor changes over time for multiple related groups can use Matplotlib to create area plots.

Matplotlib Toolkits

Although Matplotlib isn’t part of Python’s standard libraries, users can download toolkits such as the following to increase Matplotlib’s utility and functionality:

  • Mplot3d helps users create three-dimensional plots.
  • Basemap is a useful map plotting toolkit. It provides a variety of map projections, political boundaries, and even coastlines.
  • Cartopy is a mapping library that includes object-oriented map projection definitions. It also offers arbitrary line, polygon, image, and point capabilities.
  • Microsoft Excel tools allow Matplotlib to exchange data with Excel.

Real-World Scenarios for Using Matplotlib

Here are just a few of the real-world applications of Matplotlib:

  • This library allows users to plot data from a website or database.
  • Basemap can be used to plot geographical data in Matplotlib.
  • Curve fitting allows users to plot extrapolated data.
  • Data from CSV files can be plotted.
  • The data extracted by parsing an Apache log file can be plotted using Matplotlib.

When used in concert with other Python libraries and data visualization tools, Matplotlib provides users with a powerful and helpful way to analyze and visualize data.

Challenges to Using Matplotlib

Along with the many helpful features of Matplotlib, there are a few drawbacks to working with this Python library be aware of, such as:

  • Matplotlib was not designed to handle exploratory data analysis.
  • When working with more than one dataset, Matplotlib can be unwieldy.
  • Unlike Tableau, where users can create visualizations with a handful of clicks, Matplotlib requires users to write more code when generating visualizations.
  • It can be difficult to work with time series data in Matplotlib.

Despite these few challenges that present when using Matplotlib for data visualization, this Python library remains a go-to for Data Analysts.

Hands-On Programming & Data Visualization Classes

An important first step toward learning more about data analytics is enrolling in one of Noble Desktop’s data analytics classes. These beginner-friendly courses are currently available in topics such as Excel, Python, and data science, among other skills necessary for analyzing and visualizing data. Noble’s live online data visualization courses are designed for students who want to learn how to depict data in a visually appealing manner.

Noble Desktop also offers a variety of programming bootcamps for those who work with data. Courses are offered in topics like Python, JavaScript, and data science, among others. Noble’s bootcamps offer small class sizes, as well as 1-on-1 mentoring, for all participants looking to rigorously explore the most popular programming languages for data analytics.

In addition to Noble’s class listings in computer programming, there are more than 200 live online programming courses currently available from top training providers. These interactive classes are taught in real-time and provide all learners with access to an instructor who is live and ready to provide feedback and answer questions. Courses range from three hours to 72 weeks in duration and cost $149-$27,500.

Do you want to find a nearby coding class in which to enroll? If so, Noble’s Coding Classes Near Me tool provides an easy way to locate and browse over 500 coding classes currently offered in in-person and live online formats. This handy tool ensures that all interested learners can find the course that works best for them. For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool offers an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.